ECG beat classification by a novel hybrid neural network

Dokur Z., Olmez T.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, vol.66, pp.167-181, 2001 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 66
  • Publication Date: 2001
  • Doi Number: 10.1016/s0169-2607(00)00133-4
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.167-181
  • Keywords: ECG beat classification, neural networks, wavelet, genetic algorithms, WAVE-FORMS, RECOGNITION, EVENTS
  • Istanbul Technical University Affiliated: Yes


This paper presents a novel hybrid neural network structure for the classification of the electrocardiogram (ECG) beats. Two feature extraction methods: Fourier and wavelet analyses for ECG beat classification are comparatively investigated in eight-dimensional feature space. ECG features are determined by dynamic programming according to the divergence value. Classification performance, training time and the number of nodes of the multi-layer perceptron (MLP), restricted Coulomb energy (RCE) and a novel hybrid neural network are comparatively presented. In order to increase the classification performance and to decrease the number of nodes, the novel hybrid structure is trained by the genetic algorithms (GAs). Ten types of ECG beats obtained from the MIT-BIH database and from a real-time ECG measurement system are classified with a success of 96% by using the hybrid structure. (C) 2001 Elsevier Science Ireland Ltd. All rights reserved.